First 'Global Flipped Classroom in One Health': From MOOCs to research on real world challenges
Bibliographic record
Abstract
In 2016 and 2017 the first three MOOCs (Massive Online Open Course) addressing One Health were released, two of them by University of Geneva and University of Basel (Switzerland). With the support of Swiss School of Public Health and using these two highly interdisciplinary MOOCs, the first 'Global Flipped Classroom in One Health' was organized in Geneva and Basel in July 2017. This innovative event gathered 12 Swiss and international MOOC learners to work on specific public/global health challenges at the human-animal-ecosystem interface in interdisciplinary teams supported by experts from academia and international organisations (e.g. World Health Organization) based in Geneva, Basel and internationally. According to the final survey, the level of satisfaction by learners was high and they benefited from the experience in different ways: reinforcement of their knowledge and capacity to perform innovative research in One Health (e.g. using digital epidemiology), visits and meetings with experts in Global Health (e.g. World Health Organization and Institute of Global Health in Geneva, Swiss Tropical and Public Health Institute in Basel) and emerging research collaborations etc. A novel project-based learning and research model arising from MOOCs was successfully created, which offers opportunities for global education and research addressing real world challenges utilising a One Health approach.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".